AIOps-Powered Monitoring and Observability

It is impossible to analyze millions of metrics manually. eG Enterprise’s auto-baselining technology uses machine learning to automatically determine normal bounds of metrics. This technology tracks time of day, day of week behaviour of each metric and uses past history to estimate dynamic thresholds for each metric. Administrators can choose the granularity with which they apply this derived intelligence to allow thresholds to be automatically and dynamically fine-tuned.

Auto-baselining is a key to making the monitoring solution proactive and easy to use:

• Early warning alerts: Administrators get alerts to anomalies and abnormal usage of resources, unusual traffic patterns before end users are impacted.

When anomalies or problems are detected, additional diagnosis is automatically performed to collect more details about a problem. Deep domain expertise is required to determine what additional diagnosis is required. The diagnostic checks vary from one application to another and from system to system.

When a problem happens, IT admins often don’t have time to investigate. They may need to reboot a system and the problem might go away. But then the same problem could occur again … Hence, collecting detailed diagnosis automatically when a problem occurs is vitally important and eG Enterprise automates this process.

The eG Enterprise AIOps engine provides both top-to-bottom and end-to-end auto-correlation to ensure precision root-cause alerting avoiding false alarms and alert storms. The built-in intelligence that understands the relationships between IT infrastructures and applications ensures that root-causes rather than secondary symptoms are identified.

By continually processing millions of metrics and data points beyond the capabilities of a human operator, eG Enterprise ensures rapid root-cause diagnosis allowing IT staff to focus on resolving issues before business systems are impacted or the user experience of staff or customers is affected.

• eG Enterprise’s auto-discovery and dependency mapping ensures modern auto-scaled environments can be handled processing data at scale against a background of dynamic interdependencies.
• Rich topology maps of discovered architecture and components offer the operator instant observability into the configuration of their systems.
• Auto-baselining ensures the changing IT environment is continually benchmarked, enabling anomaly detection and avoiding false alerts. Thresholds for alerts are determined by machine learning algorithms including seasonal variations on an hour-by-hour, day-by-day, month-by-month and longer.